1. Technical Field
This application is related to image processing systems and methods for multispectral imagery, and more particularly, to systems and methods for removing sun-glint from multispectral imagery.
2. Related Technology
Sunlight sensed by a satellite over coastal ocean waters includes three main elements: light scattered from the atmosphere, specular light reflected off the sea surface and light that interacted with the water column and bottom emergent from the ocean.
FIG. 1 shows how the atmosphere and water surface contribute to the radiance collected by satellite imagery. Water droplets and clouds reflect sunlight, and aerosols, ozone, and other gasses absorb sunlight, changing the total amount and spectral quality of the light reaching the ocean surface. The sunlight reflects off the sea surface as “sun glint”. The sea surface acts like a mirror, so the sunlight angle of incidence equals the angle of reflection with respect to the surface normal.
In some applications, the color of the ocean itself is of interest. For example, the Naval Research Laboratory has developed a Coastal Water Spectral Toolkit to estimate water depth, bottom type and water column constituents such as chlorophyll, suspended sediments and chromophoric dissolved organic matter from the spectra of the ocean observed in hyperspectral imagery. If only images collected under optimal sun and viewing conditions, the spectral reflection and other surface effects are minimized. However, it can be useful to be able to use imagery collected under non-optimal conditions, and to remove the surface effects in post processing as much as possible.
In order to isolate the color of the ocean itself, several models exist to correct for the atmospheric component and sea glint component of the signal. Some atmospheric component models are discussed in Montes, M., Gao, B. and Davis, C., “NRL atmospheric correction algorithms for oceans: Tafkaa user's guide,” Naval Research Laboratory Tech Memo NRL/MR/7230-04-8760 (2004); Kotchenova, S., Vermote, E., Matarrese, R. and Klemm, F., “Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance,” Applied Optics, 45(26), 6763-6774 (2006); Yuan, J. and Niu, Z., “Evaluation of atmospheric correction using FLAASH,” 2008 International Workshop on Earth Observation and Remote Sensing Applications, 302-307 (2008); Berk, A., Anderson, G., Acharya, P., Bernstein, L., Muratov, L., Lee, J., Fox, M., Adler-Golden, S., Chetwynd, J., Hoke, M., Lockwood, R., Gardner, J., Cooley, T., Borel, C., Lewis, P. and Shettle, E., “MODTRAN5: 2006 update,” Proc. SPIE 6233, 62331F (2006); and Ruddick, K., Cauwer, V. and Park, Y., “Seaborne measurements of near infrared water-leaving reflectance: the similarity spectrum for turbid waters,” Limnology and Oceanography, 51(2), 1167-1179 (2006). Models that correct for sea surface reflection, for multispectral visible and near infrared imagery, are discussed in Silva, D. and Abileah, R., “Two algorithms for removing ocean surface clutter in multispectral and hyperspectral images,” Ocean Optics XIV, Kailua-Kona, Hawaii (1999); Hochberg, E., Andrefouet, S. and Tyler, M., “Sea surface correction of high spatial resolution Ikonos images to improve bottom mapping in near-shore environments” IEEE Transactions on Geoscience and Remote Sensing, 41, 1724-1729 (2003); Goodman, J., Lee, Z. and Ustin, S., “Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semianalytical model: a case study in Kaneohe Bay, Hawaii,” Applied Optics, 47, F1-F11 (2008); Hedley, J., Harbone, A. and Mumby, P., “Simple and robust removal of sun glint for mapping shallow-water benthos,” International Journal of Remote Sensing, 44, 2107-2112 (2005); Lyzenga, D., Malinas, P. and Tanis, F., “Multispectral Bathymetry using a simple physically based algorithm.” IEEE Transactions on Geoscience and Remote Sensing,” 44, 2251-2259 (2006).